store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_59448', 'seed': 59448, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[3135, 39054, 49355, 20058, 13741, 52501, 27, 48732, 50161, 46561, 55161, 57853, 6157, 54420, 12278, 44039, 39915, 42704, 25933, 631]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6587, 'nll': 2.4273316860198975}, 'chosen_samples': [39079, 28880, 21957, 11683, 18661, 57419, 37829, 38219, 3231, 26210], 'chosen_samples_score': ['1.1410968', '1.1790361', '1.144756', '1.166446', '1.2023139', '1.1601694', '1.1417913', '1.1571579', '1.173063', '1.1430194']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.7495, 'nll': 1.5801227927207946}, 'chosen_samples': [23491, 2633, 57570, 4058, 53638, 47759, 19541, 52959, 20476, 37161], 'chosen_samples_score': ['1.1499443', '1.1626782', '1.1648592', '1.1676271', '1.1938608', '1.1946976', '1.1842237', '1.1988938', '1.2443497', '1.2278284']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7469, 'nll': 1.3732210636138915}, 'chosen_samples': [20857, 1127, 19298, 5308, 39799, 57838, 58162, 56735, 11784, 28226], 'chosen_samples_score': ['1.0095459', '1.0237', '1.0247114', '1.0351156', '1.0373394', '1.0621426', '1.0621531', '1.0911438', '1.0778723', '1.0432041']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7915, 'nll': 1.0396615862846375}, 'chosen_samples': [39473, 24650, 16698, 27317, 40441, 35628, 53946, 45753, 37249, 38404], 'chosen_samples_score': ['0.9616688', '0.97433853', '0.9984261', '1.0453284', '1.035549', '0.9753928', '0.99885774', '1.0032561', '0.97536725', '0.98310524']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7727, 'nll': 1.164955770969391}, 'chosen_samples': [37313, 47081, 17420, 25309, 15853, 48654, 27174, 42141, 11476, 50639], 'chosen_samples_score': ['0.95825577', '0.9618268', '0.96270025', '0.9673091', '0.9711214', '1.0224035', '1.0317601', '0.9768733', '0.9910186', '1.0109742']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8298, 'nll': 1.0762397825717926}, 'chosen_samples': [43648, 16600, 3192, 49240, 55452, 27783, 12067, 48666, 224, 43040], 'chosen_samples_score': ['1.0123959', '1.0148904', '1.0201702', '1.0709578', '1.1114211', '1.0441082', '1.0443847', '1.0506744', '1.0659082', '1.0317621']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8309, 'nll': 0.8620005965232849}, 'chosen_samples': [32276, 40752, 21690, 45758, 22364, 54481, 47132, 47651, 13831, 17521], 'chosen_samples_score': ['0.8000345', '0.81667066', '0.8374516', '0.8582974', '0.8409716', '0.8609253', '0.83817005', '0.8884163', '1.0071168', '1.0551']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8591, 'nll': 0.857406809926033}, 'chosen_samples': [11693, 43716, 57351, 28666, 1311, 37996, 34758, 27696, 17756, 11463], 'chosen_samples_score': ['1.0103321', '1.0111473', '1.0155973', '1.012057', '1.0185814', '1.025506', '1.0388637', '1.0333245', '1.0294449', '1.0561292']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8773, 'nll': 0.7270696133375167}, 'chosen_samples': [16286, 54, 33252, 27626, 19781, 13276, 49880, 42815, 45047, 13030], 'chosen_samples_score': ['0.941073', '0.94215465', '0.94337773', '0.94424003', '1.015883', '0.9450255', '0.9591208', '0.9507058', '0.9507476', '1.0895959']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8818, 'nll': 0.737509548664093}, 'chosen_samples': [34406, 59747, 53026, 47951, 40457, 54893, 40366, 13127, 1155, 34594], 'chosen_samples_score': ['1.0172348', '1.0241605', '1.0370622', '1.0411615', '1.0459545', '1.046701', '1.0544506', '1.055205', '1.0788193', '1.2088864']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.906, 'nll': 0.629569286108017}, 'chosen_samples': [5175, 29472, 33254, 24653, 41965, 16155, 52582, 12595, 57972, 11202], 'chosen_samples_score': ['1.036718', '1.0517149', '1.0703523', '1.0721138', '1.0747945', '1.1187851', '1.1306078', '1.0931624', '1.1883607', '1.0852066']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9055, 'nll': 0.6062475979328156}, 'chosen_samples': [13365, 59395, 23806, 24521, 26444, 35232, 9640, 19868, 42112, 6130], 'chosen_samples_score': ['1.0112336', '1.0572544', '1.0163342', '1.0815479', '1.0453609', '1.0190408', '1.0307388', '1.0574336', '1.0204451', '1.071931']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8944, 'nll': 0.6171281158924102}, 'chosen_samples': [5129, 42263, 6428, 55302, 42020, 9986, 27540, 5684, 52294, 25823], 'chosen_samples_score': ['0.8696155', '0.87046206', '0.92967135', '0.96040034', '1.0363955', '0.8799536', '0.8760079', '0.87624735', '0.9009959', '0.8782696']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8969, 'nll': 0.6431762337684631}, 'chosen_samples': [181, 29335, 34739, 5429, 49121, 59314, 4873, 54646, 54065, 17948], 'chosen_samples_score': ['0.88645315', '0.8873339', '0.89132684', '0.8963142', '0.89667445', '0.9063706', '0.91523844', '0.9138353', '0.9724729', '0.9203347']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9132, 'nll': 0.5813073009252548}, 'chosen_samples': [13986, 21880, 44100, 12424, 25246, 4562, 32427, 13714, 52225, 3026], 'chosen_samples_score': ['0.9486169', '0.9765297', '0.9717615', '0.97835475', '1.009115', '0.9831379', '1.0154505', '1.0805066', '1.0188373', '1.0525775']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9225, 'nll': 0.5171409487724304}, 'chosen_samples': [22481, 42703, 13428, 4590, 12840, 16692, 45024, 24990, 14715, 3010], 'chosen_samples_score': ['0.99279344', '1.1484141', '1.0404067', '1.1006371', '1.0048082', '1.0036466', '0.99390393', '1.0564566', '0.996915', '1.0078406']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9192, 'nll': 0.5455349028110504}, 'chosen_samples': [39304, 43206, 39355, 26072, 7833, 34771, 37048, 32301, 14394, 49537], 'chosen_samples_score': ['0.9280247', '0.9287028', '0.99839', '0.9412999', '1.0228972', '0.93420964', '0.9359988', '0.9621031', '0.9788161', '1.0249896']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.926, 'nll': 0.5156634464859963}, 'chosen_samples': [57211, 16836, 34520, 32880, 35461, 2845, 1674, 49525, 51261, 14649], 'chosen_samples_score': ['0.978382', '0.97978', '0.985557', '1.0008602', '0.9971167', '1.004079', '1.021528', '1.0133703', '1.0147088', '1.0458926']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9271, 'nll': 0.5552371084690094}, 'chosen_samples': [53574, 9180, 23486, 43043, 13705, 37347, 59919, 15713, 37225, 4822], 'chosen_samples_score': ['1.0240054', '1.1542597', '1.0336945', '1.1618102', '1.0499415', '1.0742886', '1.0981973', '1.0499291', '1.0524216', '1.0718973']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9346, 'nll': 0.4988347560167313}, 'chosen_samples': [32776, 23350, 36744, 38698, 36268, 46832, 51863, 59701, 36818, 25310], 'chosen_samples_score': ['0.9968134', '1.0174665', '1.0204122', '1.0296285', '1.0822929', '1.1065991', '1.1690952', '1.1134479', '1.0502769', '1.061524']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.928, 'nll': 0.48320443630218507}, 'chosen_samples': [35401, 29132, 59390, 28192, 11616, 28056, 36760, 41361, 51047, 2148], 'chosen_samples_score': ['0.86368835', '1.0094888', '0.8788617', '0.8829581', '0.8670042', '0.8783216', '0.8908097', '0.87203604', '0.92798305', '0.8648579']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9381, 'nll': 0.42202810049057005}, 'chosen_samples': [32596, 31706, 18654, 16488, 8447, 8670, 47549, 51736, 1812, 26358], 'chosen_samples_score': ['0.8996606', '0.9005843', '0.90522325', '0.9938478', '0.9178221', '0.9362115', '0.994193', '1.0112909', '0.9887535', '1.1176231']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.937, 'nll': 0.44094925820827485}, 'chosen_samples': [47020, 31456, 19089, 12089, 51764, 10028, 19942, 8093, 1239, 10210], 'chosen_samples_score': ['0.8247808', '0.82526195', '0.84902203', '0.83515', '0.8561915', '0.8689836', '0.9068881', '0.88344365', '0.9007021', '0.87400293']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9413, 'nll': 0.429251554608345}, 'chosen_samples': [44123, 21726, 34304, 16453, 8714, 50946, 37469, 9118, 49517, 274], 'chosen_samples_score': ['1.0197484', '1.025372', '1.0262673', '1.0447416', '1.0436907', '1.0757111', '1.0947924', '1.1551492', '1.1105568', '1.0905933']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9409, 'nll': 0.43360351473093034}, 'chosen_samples': [11292, 43702, 40654, 41266, 8214, 11482, 41218, 28844, 32747, 24479], 'chosen_samples_score': ['1.0047563', '1.0148704', '1.0178039', '1.0209947', '1.0672665', '1.0886297', '1.0252421', '1.0855657', '1.0421052', '1.1978636']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9448, 'nll': 0.4238795444369316}, 'chosen_samples': [41832, 46654, 11044, 45602, 50840, 49221, 12986, 50317, 9731, 22053], 'chosen_samples_score': ['0.98004377', '0.9872143', '0.98740846', '0.9977559', '1.0241313', '1.0241531', '1.0730544', '1.0629992', '1.0418272', '1.0702739']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9515, 'nll': 0.372458253800869}, 'chosen_samples': [45443, 6466, 18598, 42642, 49364, 30478, 39576, 9501, 33150, 29938], 'chosen_samples_score': ['0.9813665', '0.9905533', '0.99213815', '1.0626712', '1.0072381', '1.0909469', '0.99255335', '1.0008956', '1.036361', '1.0185229']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9544, 'nll': 0.36406510770320893}, 'chosen_samples': [55244, 57342, 56224, 48881, 47619, 38460, 42428, 2381, 30444, 5084], 'chosen_samples_score': ['0.8703601', '0.87942106', '0.88155055', '0.89265', '0.8897736', '0.89376146', '0.8880785', '0.89653534', '0.94028896', '0.90144736']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9583, 'nll': 0.3513530597090721}, 'chosen_samples': [57742, 49282, 47443, 31954, 5790, 34946, 5898, 14894, 11797, 34101], 'chosen_samples_score': ['0.980116', '0.9910785', '0.992748', '0.9866282', '0.99902034', '1.0223415', '1.0058627', '1.0340941', '1.0189505', '1.0384476']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9551, 'nll': 0.3554642528295517}, 'chosen_samples': [29530, 18487, 16756, 8765, 30692, 12650, 13942, 42317, 12663, 506], 'chosen_samples_score': ['0.9462228', '0.95129025', '0.95615137', '0.96047074', '0.96404624', '1.0251418', '1.017673', '0.964775', '1.0560617', '0.9933832']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9616, 'nll': 0.33219326436519625}, 'chosen_samples': [44095, 29002, 30932, 22139, 58832, 13149, 36408, 4955, 12305, 22320], 'chosen_samples_score': ['0.8787721', '0.895065', '0.91353196', '1.0312201', '0.8922096', '0.8842192', '0.95531905', '1.0320673', '0.8892248', '0.89127123']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9632, 'nll': 0.3267039149999619}, 'chosen_samples': [32323, 4863, 38242, 50471, 50618, 14722, 57728, 54950, 52892, 20578], 'chosen_samples_score': ['0.9119332', '0.92866206', '0.9337765', '0.94723743', '0.93546194', '1.0019706', '0.94842243', '0.9347179', '0.93626666', '0.96786326']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.967, 'nll': 0.3140563443303108}, 'chosen_samples': [20903, 15779, 53556, 47506, 16888, 41453, 3251, 53873, 51759, 966], 'chosen_samples_score': ['1.0360672', '1.0640113', '1.0477061', '1.0744705', '1.0648433', '1.0754459', '1.1103342', '1.1759357', '1.1362374', '1.0913951']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9654, 'nll': 0.3255379095673561}, 'chosen_samples': [25835, 3810, 48360, 43950, 3692, 20169, 25910, 45069, 55739, 27139], 'chosen_samples_score': ['0.7581015', '0.76263654', '0.7694571', '0.7738259', '0.7786675', '0.78071475', '0.7936371', '0.794442', '0.89193517', '0.8313172']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9642, 'nll': 0.30325113236904144}, 'chosen_samples': [17824, 6944, 31343, 14655, 28491, 37373, 53979, 53872, 55906, 7793], 'chosen_samples_score': ['0.9212547', '0.92623657', '0.96108574', '0.9474489', '0.93258464', '0.9892701', '1.0470788', '1.0191998', '1.0095177', '0.9964187']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9628, 'nll': 0.3010858237743378}, 'chosen_samples': [49563, 41933, 3727, 8509, 54935, 15106, 49515, 46368, 46887, 7270], 'chosen_samples_score': ['0.88220024', '0.88551694', '0.89869547', '0.8997906', '0.91312927', '0.9008778', '0.91429996', '1.053715', '0.99923545', '0.94122636']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9659, 'nll': 0.2964429512619972}, 'chosen_samples': [13078, 11584, 4185, 43471, 27646, 22083, 24589, 12066, 15743, 45853], 'chosen_samples_score': ['0.85049236', '0.8674955', '0.8701242', '0.88274515', '0.8939831', '0.9015085', '0.9594866', '0.98788536', '0.913136', '0.90961033']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9663, 'nll': 0.30703048706054686}, 'chosen_samples': [23962, 15771, 854, 34328, 33856, 18324, 15912, 20186, 47597, 704], 'chosen_samples_score': ['0.95069295', '0.9942467', '0.9640676', '0.9739234', '1.0053575', '1.0736203', '1.0272682', '1.0403051', '1.094414', '1.0296977']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9683, 'nll': 0.30108374506235125}, 'chosen_samples': [109, 1618, 2450, 54195, 56014, 57659, 49890, 6582, 44442, 34968], 'chosen_samples_score': ['0.9247147', '0.9289618', '0.9307647', '0.9372875', '1.0322702', '0.93968177', '0.97188103', '0.9972929', '1.0199864', '1.0049598']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9694, 'nll': 0.3053130254149437}, 'chosen_samples': [36072, 53844, 21445, 55314, 48382, 23715, 8226, 21700, 33364, 5259], 'chosen_samples_score': ['0.872786', '0.9208586', '0.884337', '0.9020295', '0.9190548', '0.8798798', '0.92545515', '0.9980469', '0.92670727', '0.92695606']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9651, 'nll': 0.312454617023468}, 'chosen_samples': [57507, 31347, 44698, 19546, 38497, 22272, 47274, 7803, 56254, 4784], 'chosen_samples_score': ['0.9075635', '0.92740756', '0.9120293', '1.0182415', '1.0109348', '0.9242134', '0.92124665', '0.92755795', '1.0421294', '0.9125798']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.971, 'nll': 0.28187544643878937}, 'chosen_samples': [20150, 29996, 27429, 52514, 15381, 40530, 25318, 44753, 788, 8202], 'chosen_samples_score': ['0.88393205', '0.88501066', '0.8885289', '0.890476', '0.9104031', '0.9161909', '0.9069309', '0.9199355', '1.0030448', '0.9988399']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9621, 'nll': 0.30750007182359695}, 'chosen_samples': [15913, 54954, 17620, 16011, 22470, 20967, 49916, 43575, 57882, 49354], 'chosen_samples_score': ['0.87975496', '0.88317984', '0.8873015', '0.8957532', '0.9150429', '0.9272321', '0.92007804', '0.9635745', '0.92703015', '0.95243424']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9682, 'nll': 0.2854966402053833}, 'chosen_samples': [11949, 36314, 15832, 57523, 25321, 13969, 33812, 828, 17540, 17382], 'chosen_samples_score': ['0.9824642', '0.99757606', '1.0082123', '1.0096751', '1.0588697', '1.0982322', '1.0655866', '1.2112124', '1.018899', '1.0810256']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9642, 'nll': 0.3194065272808075}, 'chosen_samples': [44172, 20820, 42354, 3798, 36417, 47479, 16045, 34665, 6418, 1501], 'chosen_samples_score': ['0.95308036', '0.95913386', '0.96261084', '0.98570377', '0.97707665', '1.014205', '0.9667023', '0.9977106', '0.9674438', '0.9823594']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9677, 'nll': 0.2758099436759949}, 'chosen_samples': [46412, 25159, 31295, 46021, 33552, 9611, 14896, 52140, 20110, 47220], 'chosen_samples_score': ['0.95300496', '0.9561317', '0.9624195', '0.9684261', '1.0352392', '0.97631973', '1.0041468', '0.99486727', '0.9974481', '1.0027004']})
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store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.981, 'nll': 0.2015976920723915}, 'chosen_samples': [50426, 9392, 51414, 28305, 13524, 3730, 7058, 35632, 41464, 13729], 'chosen_samples_score': ['0.7963024', '0.7999843', '0.80459654', '0.80893314', '0.8093568', '0.8510725', '0.8949764', '0.8262618', '0.85804415', '0.8961373']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9793, 'nll': 0.20530162304639815}, 'chosen_samples': [22497, 18130, 54342, 10321, 38638, 59783, 40046, 26760, 30474, 49002], 'chosen_samples_score': ['0.76679456', '0.76830345', '0.7781406', '0.7829891', '0.7871699', '0.8899889', '0.7996227', '0.78950894', '0.79532194', '0.8061001']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9783, 'nll': 0.21561999917030333}, 'chosen_samples': [52138, 45658, 17296, 57026, 12078, 31650, 52087, 47888, 23588, 34765], 'chosen_samples_score': ['0.723462', '0.7335677', '0.77279943', '0.90430826', '0.8224232', '0.8028231', '0.73917437', '0.72921425', '0.85766774', '0.7810591']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9852, 'nll': 0.18066761195659636}, 'chosen_samples': [29730, 52968, 49012, 8207, 5559, 38920, 19328, 25783, 32918, 39309], 'chosen_samples_score': ['0.80317485', '0.806831', '0.810986', '0.8188397', '0.8215748', '0.82482076', '0.8369967', '0.8442641', '0.89389724', '0.90993696']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9822, 'nll': 0.20031771659851075}, 'chosen_samples': [8200, 40660, 24542, 57793, 39672, 1137, 5278, 148, 33062, 1175], 'chosen_samples_score': ['0.82324135', '0.83756995', '0.8453542', '0.84300303', '0.8549996', '0.858424', '0.8635176', '0.9013392', '0.92203736', '0.86289716']})
